CN112017256A - Online CT image quality free customization method and computer readable storage medium - Google Patents
Online CT image quality free customization method and computer readable storage medium Download PDFInfo
- Publication number
- CN112017256A CN112017256A CN202010892871.4A CN202010892871A CN112017256A CN 112017256 A CN112017256 A CN 112017256A CN 202010892871 A CN202010892871 A CN 202010892871A CN 112017256 A CN112017256 A CN 112017256A
- Authority
- CN
- China
- Prior art keywords
- sample
- parameters
- parameter
- filter
- image
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 29
- 235000012431 wafers Nutrition 0.000 claims abstract description 11
- 238000001914 filtration Methods 0.000 claims description 12
- 230000009467 reduction Effects 0.000 claims description 7
- 238000004364 calculation method Methods 0.000 claims description 3
- 238000003707 image sharpening Methods 0.000 claims description 3
- 230000002194 synthesizing effect Effects 0.000 claims description 3
- 238000002591 computed tomography Methods 0.000 abstract description 29
- 230000000694 effects Effects 0.000 abstract description 6
- 230000008569 process Effects 0.000 abstract description 4
- 238000002059 diagnostic imaging Methods 0.000 abstract description 2
- 238000003384 imaging method Methods 0.000 description 6
- 238000001514 detection method Methods 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 239000011159 matrix material Substances 0.000 description 2
- 230000004044 response Effects 0.000 description 2
- 230000006978 adaptation Effects 0.000 description 1
- 230000015572 biosynthetic process Effects 0.000 description 1
- 230000001010 compromised effect Effects 0.000 description 1
- 230000002354 daily effect Effects 0.000 description 1
- 238000003745 diagnosis Methods 0.000 description 1
- 238000002595 magnetic resonance imaging Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000003786 synthesis reaction Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T11/00—2D [Two Dimensional] image generation
- G06T11/003—Reconstruction from projections, e.g. tomography
- G06T11/005—Specific pre-processing for tomographic reconstruction, e.g. calibration, source positioning, rebinning, scatter correction, retrospective gating
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T11/00—2D [Two Dimensional] image generation
- G06T11/003—Reconstruction from projections, e.g. tomography
- G06T11/008—Specific post-processing after tomographic reconstruction, e.g. voxelisation, metal artifact correction
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/70—Denoising; Smoothing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/73—Deblurring; Sharpening
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10072—Tomographic images
- G06T2207/10081—Computed x-ray tomography [CT]
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/30—Computing systems specially adapted for manufacturing
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Image Processing (AREA)
- Apparatus For Radiation Diagnosis (AREA)
Abstract
The invention discloses an online CT image quality free customization method and a computer readable storage medium, belonging to the technical field of medical imaging, and the method comprises the following steps: scanning to obtain original CT data of a part to be detected; generating different types of sample wafers according to original CT data and different parameter conditions to form a sample wafer library; searching the best candidate sample or generating the best sample from the samples for calculating new target parameters; and taking the obtained target parameter as a new scanning parameter, and automatically updating the new scanning parameter into the image chain system. The method adopted by the invention can meet different requirements of users on images, automatically customizes corresponding image reconstruction parameters in the CT scanning process, updates new scanning parameters into an image chain system in real time, has automatic updating operation, high scanning efficiency and good image output effect, and does not need manufacturers to additionally add a large amount of parameters such as original convolution kernels and the like in the CT scanning system.
Description
Technical Field
The invention relates to the technical field of medical imaging, in particular to an online CT image quality free customization method and a computer readable storage medium.
Background
CT (computed tomography) system scans object with X-ray to obtain projection data, and processes the projection data by fault reconstruction algorithm to obtain fault and three-dimensional density information of object, so as to achieve the purpose of nondestructive detection. CT detection has important application in the fields of medical diagnosis, industrial nondestructive detection and the like. In the field of medical diagnostics, CT has been known since 1970 as a three-key imaging system for medical use, along with Magnetic Resonance Imaging (MRI), positron emission computed tomography (PET) and CT combined systems (PET/CT). Compared with other imaging means, the CT reconstruction can quickly obtain high-resolution images, the contrast precision of the reconstruction result can be controlled within 1%, and objects of 0.5mm level can be distinguished.
Due to the complexity of the imaging physics, even the most advanced CT systems deal with the impact of various image artifacts on the final image quality. In order to more clearly display the information of various parts and tissue structures of a patient, different pre-adjusted filtering parameters are generally required to reconstruct an image for one scan so as to meet the requirements of doctors. However, due to the complexity of the filtering parameters, the preferences of each doctor and the use habits, the pre-factory set convolution is difficult to meet the requirements of all users. Typical image quality factors include many, such as image resolution, noise texture, etc. These parameters are also linked to each other for a given imaging system, and a single index may or may not reflect the best image quality, and may need to be balanced to achieve the target image quality.
In the prior art, for important parameters of reconstruction: the convolution kernels are all designed in advance by manufacturers, and users can only select the convolution kernels from the lists and cannot modify the convolution kernels by themselves. Therefore, the images available to the user are pre-designed. Although as many convolution kernels as possible can be provided, it is difficult to meet the requirements of all users. Moreover, the tissue of interest to the physician varies from site to site, and in order to optimize image quality, the patient typically needs to be scanned and reconstructed using different parameters. It is not practical for CT manufacturers to provide hundreds or thousands of convolution kernels, since each time the operator selects the commonly used ones from among such many kernels, the ease of use is compromised.
Disclosure of Invention
The technical purpose is as follows: aiming at the technical problems, the invention discloses an online CT image quality free customization method and a computer readable storage medium, which can realize that a user customizes various image styles of different systems according to different requirements in the CT scanning process, automatically updates and generates a new filtering and noise reduction algorithm required by imaging, and has convenient operation and good image imaging effect.
The technical scheme is as follows: in order to achieve the technical purpose, the invention adopts the following technical scheme:
an online CT image quality free customization method is characterized by sequentially executing the following steps:
s1, scanning to obtain original CT data of the part to be detected;
s2, generating different types of sample wafers according to the original CT data and different parameter conditions to form a sample wafer library;
s3, searching the best candidate sample or generating the best sample from the samples, and calculating a new target parameter:
if the sample meeting the preset conditions exists in the sample library, taking all the sample meeting the preset conditions as the optimal candidate samples, and synthesizing according to the parameters corresponding to all the optimal candidate samples to obtain new target parameters;
if no sample meeting the preset conditions exists in the sample library, selecting any sample, processing the selected sample through an image adjusting tool, taking the adjusted image as an optimal sample, and automatically estimating according to the optimal sample to obtain a new target parameter;
s4, the target parameters obtained in step S3 are automatically updated to the video chain system as new scan parameters.
Preferably, the parameters in step S2 include reconstruction kernel, denoising strength and enhancement strength, and the swatch type includes different spatial resolutions, different noise strengths and different enhancement strengths.
Preferably, a new target parameter is synthesized by using an interpolation or fitting mode according to the parameters corresponding to all the optimal candidate samples; the image adjusting method used for obtaining the optimal sample comprises image sharpening, image blurring or noise reduction processing.
Preferably, the calculation formula of the filtering parameter is:
filter＝filter1*w1+filter2*w2+…+filtern*wn
wherein the filter is a filter parameter specified by the end usernRepresenting the filter parameter, w, of the nth samplenA weight indicating the nth sample designated by the user
Preferably, in S3, the calculation formula for obtaining the new filter parameter according to the optimal sample slice automatic estimation is as follows:
wherein, the filteroriF (w) is the original filtering parameter, and f (w) is the sharpening parameter.
The invention also discloses a computer readable storage medium, which is characterized in that: the computer readable storage medium stores at least one instruction executable by a processor, wherein the at least one instruction, when executed by the processor, is for performing the online CT image quality free-customization method.
Has the advantages that: due to the adoption of the technical scheme, the invention has the following technical effects:
the online CT image quality free customization method disclosed by the invention meets different requirements of users on images through a self-adaptive method, helps the users to customize corresponding image reconstruction parameters independently, the parameters comprise convolution kernel and noise reduction processing parameters, a CT manufacturer does not need to preset a large number of parameters such as convolution kernel and the like in a CT system, and the method can automatically update the obtained new scanning parameters into an image chain system in real time in the scanning process, so that the operation requirements on CT scanning workers are reduced, the scanning efficiency is high, and the image output effect is good.
Drawings
FIG. 1 is an overall flow chart of the online CT image quality free customization method of the present invention;
FIG. 2 is a graph of spatial frequency versus frequency response for different filter parameters;
FIG. 3 is an effect diagram of different filter parameters corresponding to different reconstructed image qualities; wherein (a) corresponds to filter parameter 1 in fig. 2 and (b) corresponds to filter parameter 2 in fig. 2;
FIG. 4 is a graph showing the relationship between the denoising intensity and the image noise under different denoising parameters;
FIG. 5 is an effect diagram of different denoising parameters corresponding to different image noise levels; wherein (c) corresponds to denoising parameter 1 in fig. 2, and (d) corresponds to denoising parameter 2 in fig. 2;
FIG. 6 is a schematic diagram of a scanning protocol required for a user to generate 3 dailies;
FIG. 7 is a schematic illustration of a user-customized graphical interface;
fig. 8 shows images with different sharpening parameters, which correspond to three situations, from left to right, of no sharpening, 0.1 sharpening parameter, and 0.2 sharpening.
Detailed Description
As shown in FIG. 1, the invention discloses a free customization method for online CT image quality, which comprises the following specific steps:
1) and scanning to obtain the original data of the corresponding part.
2) And generating a plurality of types of pictures as a sample library according to the scanning data and different parameters, such as reconstruction kernel, denoising strength, enhancement strength, reconstruction layer thickness, reconstruction matrix size and the like. The difference between different types of samples in the sample library may be different spatial resolutions, different noise intensities, different enhancement intensities, and the like.
3) And selecting the sample wafer which is closest to the requirement by the user.
4) If the user can find out the sample with proper requirement or approximate requirement, such as 2-3 sample, the sample is used as the best candidate sample; if it cannot be found, go to step 7).
5) And the final scanning protocol parameters comprise different parameters, such as reconstruction kernel, denoising strength, enhancement strength, reconstruction layer thickness, reconstruction matrix size and the like, and are synthesized through the parameters corresponding to the optimal candidate sample wafer.
The synthesis mode can select interpolation, fitting and the like. Fig. 2-3 show the relationship between spatial frequency and frequency response under different filter parameters. The final filter parameters can be obtained using linear interpolation:
filter＝filter1*w1+filter2*w2+…+filtern*wn
in the above formula, the filter is a filter parameter specified by the end usernRepresenting the filter parameter, w, of the nth samplenRepresents the weight of the nth sample designated by the user and satisfies the condition
Similarly, as shown in fig. 4-5, the denoising parameters and enhancement parameters specified by the end user can also be obtained in the same manner.
6) And automatically updating the new parameters obtained in the step 5), such as the filtering parameters, the noise reduction parameters, the enhancement parameters and the like, to the image chain system.
7) And in step 4, if there are no samples meeting the user's requirements in the sample library, the user may modify the samples through the image adjustment tool, as shown in fig. 8. The image adjusting method comprises image sharpening, image blurring, noise reduction and the like.
8) And (7) adjusting the sample wafer according to the step (7) to obtain the required optimal sample wafer.
9) And automatically estimating reconstruction parameters according to the optimal sample wafer. In fig. 4, the sharpening parameter f (w) is selected, and the new filtering parameter filter is the original filtering parameter filteroriConvolution with sharpening parameter f (w).
Image changes are as in fig. 5:
simultaneously recording an image enhancement parameter of fenh(w) and denoising parameter fdenoise(w)。
10) New parameters obtained in the step 9), such as a filtering parameter filter and a noise reduction parameter fdenoise(w), enhancement parameter fenh(w), etc., automatically updating to the video chain system.
11) And updating the scanning parameters updated through the steps to a scanning/reconstruction system of the current user to generate customized scanning reconstruction parameters suitable for the current user. In future use by users, the generated images are based on the user-defined parameters.
The scanning parameters updated in the step 6) or the step 10) can balance various parameter conditions in the CT scanning system, and automatically select proper scanning parameters aiming at CT scanning of different parts to obtain a CT scanning image with better quality.
The above description is only of the preferred embodiments of the present invention, and it should be noted that: it will be apparent to those skilled in the art that various modifications and adaptations can be made without departing from the principles of the invention and these are intended to be within the scope of the invention.
Claims (7)
1. An online CT image quality free customization method is characterized by sequentially executing the following steps:
s1, scanning to obtain original CT data of the part to be detected;
s2, generating different types of sample wafers according to the original CT data and different parameter conditions to form a sample wafer library;
s3, searching the best candidate sample or generating the best sample from the samples, and calculating a new target parameter:
if the sample meeting the preset conditions exists in the sample library, taking all the sample meeting the preset conditions as the optimal candidate samples, and synthesizing according to the parameters corresponding to all the optimal candidate samples to obtain new target parameters;
if no sample meeting the preset conditions exists in the sample library, selecting any sample, processing the selected sample through an image adjusting tool, taking the adjusted image as an optimal sample, and automatically estimating according to the optimal sample to obtain a new target parameter;
s4, the target parameters obtained in step S3 are automatically updated to the video chain system as new scan parameters.
2. The on-line CT image quality free-customization method according to claim 1, characterized in that: the parameters in step S2 include reconstruction kernel, denoising strength, and enhancement strength, and the swatch type includes different spatial resolutions, different noise strengths, and different enhancement strengths.
3. The on-line CT image quality free-customization method according to claim 1, characterized in that: in step S3, the target parameters include reconstruction parameters, denoising parameters, and enhancement parameters, and the new scan parameters include new filtering parameters, denoising parameters, and enhancement parameters.
4. The on-line CT image quality free-customization method according to claim 1, characterized in that: in step S3, synthesizing new target parameters by using interpolation or fitting method according to the parameters corresponding to all the best candidate samples; the image adjusting method used for obtaining the optimal sample comprises image sharpening, image blurring or noise reduction processing.
5. The on-line CT image quality free-customization method according to claim 4, characterized in that: in step S3, the filter parameter is calculated as:
filter＝filter1*w1+filter2*w2+…+filtern*wn
wherein the filter is a filter parameter specified by the end usernRepresenting the filter parameter, w, of the nth samplenA weight indicating the nth sample designated by the user
6. The on-line free customization method for CT image quality according to claim 1, wherein in S3, the calculation formula for obtaining the new filtering parameters according to the best sample auto-estimation is:
wherein, the filteroriF (w) is the original filtering parameter, and f (w) is the sharpening parameter.
7. A computer-readable storage medium characterized by: the computer readable storage medium stores at least one instruction executable by a processor, wherein the at least one instruction, when executed by the processor, is configured to perform the online CT image quality free customization method according to any one of claims 1 to 6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010892871.4A CN112017256B (en) | 2020-08-31 | 2020-08-31 | On-line CT image quality free customization method and computer readable storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010892871.4A CN112017256B (en) | 2020-08-31 | 2020-08-31 | On-line CT image quality free customization method and computer readable storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112017256A true CN112017256A (en) | 2020-12-01 |
| CN112017256B CN112017256B (en) | 2023-09-15 |
Family
ID=73503899
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010892871.4A Active CN112017256B (en) | 2020-08-31 | 2020-08-31 | On-line CT image quality free customization method and computer readable storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112017256B (en) |
Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102488527A (en) * | 2004-11-16 | 2012-06-13 | 梅德拉股份有限公司 | Modeling of pharmaceutical propagation |
| US20140098933A1 (en) * | 2012-10-04 | 2014-04-10 | General Electric Company | Dual display ct scanner user interface |
| EP3133555A2 (en) * | 2015-08-17 | 2017-02-22 | FEI Company | Novel data processing in a tomographic imaging apparatus |
| US20180096477A1 (en) * | 2015-04-13 | 2018-04-05 | Accumetra, Llc | Automated scan quality monitoring system |
| CN107948637A (en) * | 2017-12-14 | 2018-04-20 | 上海联影医疗科技有限公司 | A kind of die body picture quality detection method and system |
| US20180322617A1 (en) * | 2015-11-10 | 2018-11-08 | Koninklijke Philips N.V. | Method for automatic optimization of quantitative map generation in functional medical imaging |
| US20190035117A1 (en) * | 2017-07-25 | 2019-01-31 | Nuctech Company Limited | Method and device for reconstructing ct image and storage medium |
| US20190104940A1 (en) * | 2017-10-06 | 2019-04-11 | Toshiba Medical Systems Corporation | Apparatus and method for medical image reconstruction using deep learning for computed tomography (ct) image noise and artifacts reduction |
| US20190122343A1 (en) * | 2017-10-25 | 2019-04-25 | Shenyang Neusoft Medical Systems Co., Ltd. | Denoising ct image |
| US20190206095A1 (en) * | 2017-12-29 | 2019-07-04 | Tsinghua University | Image processing method, image processing device and storage medium |
| CN110023999A (en) * | 2016-11-29 | 2019-07-16 | 皇家飞利浦有限公司 | The ultrafast reconstruction of Interactive Object in transmitting and transmission tomography |
| CN110503698A (en) * | 2018-05-16 | 2019-11-26 | 西门子医疗有限公司 | The method for determining image mode and its parameter, device and imaging system |
| US20190392615A1 (en) * | 2018-06-26 | 2019-12-26 | Uih America, Inc. | System and method for 3d image reconstruction from axial step-and-shoot ct |
-
2020
- 2020-08-31 CN CN202010892871.4A patent/CN112017256B/en active Active
Patent Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102488527A (en) * | 2004-11-16 | 2012-06-13 | 梅德拉股份有限公司 | Modeling of pharmaceutical propagation |
| US20140098933A1 (en) * | 2012-10-04 | 2014-04-10 | General Electric Company | Dual display ct scanner user interface |
| US20180096477A1 (en) * | 2015-04-13 | 2018-04-05 | Accumetra, Llc | Automated scan quality monitoring system |
| EP3133555A2 (en) * | 2015-08-17 | 2017-02-22 | FEI Company | Novel data processing in a tomographic imaging apparatus |
| US20180322617A1 (en) * | 2015-11-10 | 2018-11-08 | Koninklijke Philips N.V. | Method for automatic optimization of quantitative map generation in functional medical imaging |
| CN110023999A (en) * | 2016-11-29 | 2019-07-16 | 皇家飞利浦有限公司 | The ultrafast reconstruction of Interactive Object in transmitting and transmission tomography |
| US20190035117A1 (en) * | 2017-07-25 | 2019-01-31 | Nuctech Company Limited | Method and device for reconstructing ct image and storage medium |
| US20190104940A1 (en) * | 2017-10-06 | 2019-04-11 | Toshiba Medical Systems Corporation | Apparatus and method for medical image reconstruction using deep learning for computed tomography (ct) image noise and artifacts reduction |
| US20190122343A1 (en) * | 2017-10-25 | 2019-04-25 | Shenyang Neusoft Medical Systems Co., Ltd. | Denoising ct image |
| CN107948637A (en) * | 2017-12-14 | 2018-04-20 | 上海联影医疗科技有限公司 | A kind of die body picture quality detection method and system |
| US20190206095A1 (en) * | 2017-12-29 | 2019-07-04 | Tsinghua University | Image processing method, image processing device and storage medium |
| CN110503698A (en) * | 2018-05-16 | 2019-11-26 | 西门子医疗有限公司 | The method for determining image mode and its parameter, device and imaging system |
| US20190392615A1 (en) * | 2018-06-26 | 2019-12-26 | Uih America, Inc. | System and method for 3d image reconstruction from axial step-and-shoot ct |
Non-Patent Citations (2)
| Title |
|---|
| AAKER, GD等: "Interactive Visualization of the Retina in a 3-D Virtual Reality Environment", 《INVESTIGATIVE OPHTHALMOLOGY & VISUAL SCIENCE 》 * |
| 胡洁等: "新型非局部平均滤波修正的CT图像重建算法", 《计算机工程与设计》, vol. 37, no. 10 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112017256B (en) | 2023-09-15 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| JP6100772B2 (en) | Image processing method and computing apparatus | |
| JP5665903B2 (en) | Image processing apparatus and method, image processing system, and program | |
| JP5525797B2 (en) | Medical image processing apparatus, ultrasonic diagnostic apparatus, and medical image processing method | |
| CN107464231B (en) | System and method for determining optimal operating parameters for medical imaging | |
| CN111815735B (en) | Human tissue self-adaptive CT reconstruction method and reconstruction system | |
| JP2007203046A (en) | Method and system for preparing image slice of object | |
| JP6296942B2 (en) | CT image evaluation apparatus and CT image evaluation method | |
| CN113436290A (en) | Method and system for selectively removing streak artifacts and noise from images using a deep neural network | |
| EP3326533B1 (en) | Tomographic device and tomographic image processing method according to same | |
| US20020122576A1 (en) | Method and device for the registration of images | |
| US9320488B2 (en) | Method and device for correction of movement artifacts in a computed tomography image | |
| US8194945B2 (en) | Computer aided image acquisition and diagnosis system | |
| US10013778B2 (en) | Tomography apparatus and method of reconstructing tomography image by using the tomography apparatus | |
| US10964074B2 (en) | System for harmonizing medical image presentation | |
| JP2007503904A (en) | Adaptive image uniformity correction for high-field magnetic resonance imaging | |
| US20050143639A1 (en) | Medical image processing apparatus, ROI extracting method and program | |
| JP6423094B2 (en) | Diagnostic imaging apparatus and method | |
| JP2008104798A (en) | Image processing method | |
| CN112017256B (en) | On-line CT image quality free customization method and computer readable storage medium | |
| EP4315250A1 (en) | Systems and methods for multi-kernel synthesis and kernel conversion in medical imaging | |
| US11798205B2 (en) | Image reconstruction employing tailored edge preserving regularization | |
| JP6358244B2 (en) | MEDICAL IMAGE PROCESSING DEVICE, MEDICAL IMAGE PROCESSING DEVICE CONTROL METHOD, AND PROGRAM | |
| JP2006026396A (en) | Image processing system and method, control program, and storage medium | |
| EP4365828A1 (en) | System and method for employing residual noise in deep learning denoising for x-ray imaging | |
| JP7086630B2 (en) | Information processing equipment, information processing methods, and programs |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |